• What are the challenges Garry, Larry, and Harry encounter in doing their job, when working with text data?

  • Go to www.menti.com and use the code 22 07 62 0

• Huge amount of data

• High dimensional but sparse

  • all possible word and phrase types in the language!!

• Noisy data

  • Examples: Abbreviations, spelling errors, short text

• Complex relationships between words

  • “Hema merges with Intertoys”

  • “Intertoys is bought by Hema”

Back to the story

• Which can be a part of Text Mining definition?
  • The discovery by computer of new, previously unknown information from textual data
  • Automatically extracting information from text
  • Text mining is about looking for patterns in text
  • Text mining describes a set of techniques that model and structure the information content of textual sources

(You can choose multiple choices)

Go to www.menti.com and use the code 22 07 62 0

• “the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources” Hearst (1999)

• Text mining is about looking for patterns in text, in a similar way that data mining can be loosely described as looking for patterns in data.

• Text mining describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources. (Wikipedia)

• You can view my screen on your machine:

  • join.me/r_tm

• Access the course materials from:

  • http://www.ayoubbagheri.nl/r_tm

• The course teaches students text mining techniques using R on a variety of applications in many domains of science.

• During the lecture

  • Post your question to the chat; we will read them during a break.

• During the computer lab

  • Post your question in general; we will answer them.

• After the lecture

  • Feel free to send me an email or text me in Microsoft Teams

• Automatically classify political news from sports news

• Authorship identification

• Age/gender identification

• Language Identification

• Sentiment analysis

• …

https://www.nature.com/articles/s41746-021-00404-9

• https://asreview.nl/

• https://github.com/asreview/asreview

• https://github.com/asreview

• Text classification
• Text clustering

We will also cover:

• Sentiment analysis
• Feature selection
• Topic modelling
• Word embedding
• Deep learning models
• Responsible text mining
• Text summarization

• Supervised learning
• Human experts annotate a set of text data
  • Training set
• Learn a classification model

• Which problem is not a text classification task? (less likely to be)

  • Author’s gender detection from text

  • Finding about the smoking conditions of patients from clinical letters

  • Grouping news articles into political vs non-political news

  • Classifying reviews into positive and negative sentiment

Go to www.menti.com and use the code 86 08 86 5

• Unsupervised learning
• Finding Groups of Similar Documents
• No labeled data

• Which problem is not a text clustering task? (less likely to be)

  • Grouping similar news articles

  • Grouping discharge letters in two categories: heart disease vs cancer

  • Grouping tweets which support Trump into three undefined subgroups

  • Grouping online books of a library in 10 categories

Go to www.menti.com and use the code 86 08 86 5

• Very powerful and quite cryptic

• Fun once you understand them

• Regular expressions are a language unto themselves

• A language of “marker characters” - programming with characters

• It is kind of an “old school” language - compact

• A formal language for specifying text strings

• How can we search for any of these?

  • woodchuck

  • woodchucks

  • Woodchuck

  • Woodchucks

• Letters inside square brackets []

• Ranges [A-Z]

• Negations [^Ss]

  • Carat means negation only when first in []

• Woodchucks is another name for groundhog!
• The pipe | for disjunction

• Find me all instances of the word “the” in a text.

Misses capitalized examples

Incorrectly returns other or theology

[^a-zA-Z] [tT]he [^a-zA-Z]

• The process we just went through was based on fixing two kinds of errors

  • Matching strings that we should not have matched ( there, then, o ther)

    • False positives (Type I)

  • Not matching things that we should have matched (The)

    • False negatives (Type II)

• In NLP we are always dealing with these kinds of errors.

• Reducing the error rate for an application often involves two antagonistic efforts:

  • Increasing accuracy or precision (minimizing false positives)

  • Increasing coverage or recall (minimizing false negatives).

• Regular expressions play a surprisingly large role

  • Sophisticated sequences of regular expressions are often the first model for any text processing text

• For many hard tasks, we use machine learning classifiers

  • But regular expressions are used as features in the classifiers

  • Can be very useful in capturing generalizations

• Regular expressions are a cryptic but powerful language for matching strings and extracting elements from those strings

• Regular expressions have special characters that indicate intent